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Add Amazon Sagemaker Model
Prerequisites
To get started with AWS SageMaker, you need to meet the following prerequisites:
AWS Account: You must have an active AWS account. If you do not have one, you can create a new account on the AWS website.
IAM Permissions: Ensure that your AWS Identity and Access Management (IAM) user has the necessary permissions to access AWS SageMaker. You may need permissions for related services such as Amazon S3, AWS Lambda, and others.
AWS SDK or CLI: Familiarity with the AWS SDK (e.g., Boto3 for Python) or the AWS Command Line Interface (CLI) to interact with SageMaker.
Data Storage: Set up an Amazon S3 bucket to store your training data and model artifacts, as SageMaker uses S3 for data input and output.
Understanding of Machine Learning: A basic understanding of machine learning concepts and workflows, as well as familiarity with the specific algorithms you plan to use.
Programming Environment: Set up a programming environment where you can write and execute your code, such as Jupyter notebooks or a local development environment.
For more detailed information, you can refer to the official AWS SageMaker documentation: AWS SageMaker Documentation.
Post the Prerequisites are met, you can proceed to configure the model. You will need to add the following details to add a model by Amazon Sagemaker -
Embeddings/ReRanker
Select the type of model you want to configure.
Embeddings: Select this if the SageMaker model generates vector embeddings
Reranker: Select this if the model is used to reorder a list of results based on relevance
Model Id
A dropdown field listing available SageMaker model IDs deployed in your Amazon account.
Display Name
A user-friendly name that will appear in the Model Hub or selection dropdowns. This name is for display purposes only and does not affect runtime behavior.
Endpoint
The fully qualified SageMaker endpoint URL is used to invoke the model. Must correspond to the chosen model ID. Note: This is critical for routing inference requests to SageMaker.